Text Mining/Analytics and Qualitative Approaches
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University Vaccination Expectations as an Example

About this workshop

Using the same texts, this workshop applies both text mining/analytics (TM/A) and qualitative data analysis (QDA) approaches to examine the documents on vaccination expectations in a sample of U.S. universities across “blue” and “red” states.

This workshop consists of two parts. The first part introduces text mining/analytics techniques using a web-interfacing tool, Voyant, for exploratory analysis. The second part introduces qualitative data analysis using an open source software, Taguette. Specifically, this part relies on the grounded theory to develop a mixed method approach to analyze the texts.

The two sessions, held back-to-back, compare and contrast the TM/A and QDA approaches towards the same materials through demonstration and hands-on exercises. No prior knowledge of either method is required.


Learning objectives

  • Understanding the quantitative and qualitative approaches of handling textual data, theoretically and practically
  • Navigating the Voyant interface for exploratory text analysis
  • Using Taguette for qualitative coding and aggregating textual data
  • Applying mixed methods to outputs from Taguette to generate insights


Contact

Yun Dai (yun.dai@nyu.edu), Data Services Librarian, NYU Shanghai
Fan Luo (fan.luo@nyu.edu), Digital Scholarship Manager, NYU Shanghai